spark-python-data-source

To create a custom PySpark DataSource, you implement a DataSource entry point, base readers and writers, and concrete Batch or Stream implementations. This connects Spark to external systems like APIs, databases, or queues for batch and streaming integration.

Yes, you can stream REST API data into a lakehouse by implementing a custom PySpark DataSource. The Skill provides concrete Stream implementations and guidance for partitioning and type conversion to support streaming data integration tasks.

The best way to structure a production PySpark DataSource is using a flat inheritance model with in-executor imports and minimal dependencies. This pattern ensures explicit error handling, security, and production-readiness across common data-integration scenarios.

Yes, building a custom Spark Python data source supports both batch and streaming modes. You create concrete Batch and Stream implementations alongside base Reader and Writer classes to handle data integration across different processing paradigms.

You handle authentication and partitioning in a PySpark data connector by following explicit patterns provided within the concrete Batch and Stream implementations. The Skill offers specific guidance for these requirements to ensure secure and efficient data access.

In-executor imports are recommended when building PySpark DataSources to maintain minimal dependencies and a flat inheritance model. This approach prevents dependency conflicts and ensures robust error handling across distributed Spark environments.